Add application file

app.py

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+import gradio as gr
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
+from keras.preprocessing import image
+
+def preprocess_image(image_path, target_size=(500, 500)):
+    img = Image.open(image_path).convert('L')  # Convert to grayscale
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array.astype('float32') / 255.0  # Normalize pixel values between 0 and 1
+    return img_array
+
+def denoise_image(image_path):
+    # Load the denoising autoencoder model
+    model = load_model('model.h5')
+    
+    # Preprocess the image
+    img_array = preprocess_image(image_path)
+    
+    # Denoise the image using the autoencoder model
+    denoised_img = model.predict(img_array)
+    denoised_img = np.squeeze(denoised_img)  # Remove batch dimension
+    
+    # Convert the denoised image array to PIL Image
+    denoised_img = (denoised_img * 255).astype(np.uint8)
+    denoised_img_pil = Image.fromarray(denoised_img)
+    
+    return denoised_img_pil
+
+image_interface = gr.Interface(
+    fn=denoise_image,
+    inputs=gr.Image(sources=['upload'], type='filepath', label='Upload Image'),
+    outputs=gr.Image(label='Denoised Image', type='pil', height=500, width=500),
+    title='Image Denoiser',
+    description='Upload an image to denoise.',
+    allow_flagging=False
+)
+
+image_interface.launch()
+

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d0ffb13b3eb1fb8829d0b9ddbdfd6d8adcbdefeed7f6863a5c4100d1def07748
+size 4142984